--- title: "FunASR vs awesome-whisper" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/modelscope-funasr-vs-sindresorhus-awesome-whisper" tools: ["modelscope-funasr", "sindresorhus-awesome-whisper"] --- # FunASR vs awesome-whisper *GraphCanon updated Aug 16, 2026* ## Verdict Pick FunASR if funASR is an industrial-grade toolkit supporting real-time speech recognition across over 50 languages; pick awesome-whisper if awesome-whisper is an organized repository aggregating resources for OpenAI's Whisper AI-powered speech recognition system, covering models, apps, bindings, packages, and community tools. [FunASR](https://github.com/modelscope/FunASR) reports 20k GitHub stars, 2.0k forks, and 5 open issues, last pushed Jul 30, 2026. [awesome-whisper](https://github.com/sindresorhus/awesome-whisper) has 2.4k stars, 156 forks, and 7 open issues, last pushed Mar 17, 2026. Figures are from public GitHub metadata via [FunASR's repository](https://github.com/modelscope/FunASR) and [awesome-whisper's repository](https://github.com/sindresorhus/awesome-whisper). | | [FunASR](/tools/modelscope-funasr.md) | [awesome-whisper](/tools/sindresorhus-awesome-whisper.md) | | --- | --- | --- | | Tagline | Industrial-grade speech recognition toolkit | Curated resources for Whisper speech recognition system | | Stars | 19,554 | 2,361 | | Forks | 1,965 | 156 | | Open issues | 5 | 7 | | Language | Python | - | | Adopt for | FunASR is an industrial-grade toolkit supporting real-time speech recognition across over 50 languages. | awesome-whisper is an organized repository aggregating resources for OpenAI's Whisper AI-powered speech recognition system, covering models, apps, bindings, packages, and community tools. | | Persona | - | - | | Runtime | - | - | | License | MIT License allows free use and modification. | CC0-1.0 | | Categories | Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FunASR](/tools/modelscope-funasr.md) | [awesome-whisper](/tools/sindresorhus-awesome-whisper.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 134d | | Open issues (now) | 5 | 7 | | Owner type | Organization | User | | Full report | [trust report](/tools/modelscope-funasr/trust.md) | [trust report](/tools/sindresorhus-awesome-whisper/trust.md) | ## Decision facts: FunASR - **Requirements:** Requires Python ≥ 3.8.; PyTorch and torchaudio must be installed separately before running FunASR. - **Adopt for:** FunASR is an industrial-grade toolkit supporting real-time speech recognition across over 50 languages. - **License detail:** MIT License allows free use and modification. - **Runtime:** unknown ## Decision facts: awesome-whisper - **Adopt for:** awesome-whisper is an organized repository aggregating resources for OpenAI's Whisper AI-powered speech recognition system, covering models, apps, bindings, packages, and community tools. ## Choose when ### Choose FunASR if… - License: FunASR is MIT, awesome-whisper is CC0-1.0. - Requirements: Requires Python ≥ 3.8.; PyTorch and torchaudio must be installed separately before running FunASR.. - Tags unique to FunASR: asr, audio, chinese, emotion-recognition. - When requiring high-speed real-time processing of up to 170x realtime speech recognition. ### Choose awesome-whisper if… - License: awesome-whisper is CC0-1.0, FunASR is MIT. - Tags unique to awesome-whisper: ai, artificial-intelligence, gpt, openai. - When seeking curated information on Whisper variants optimized for various platforms and languages ## When NOT to use FunASR - In scenarios where only a narrow set of languages are required, as overhead for supporting over 50 languages may be unnecessary. - For projects lacking GPU resources since certain models, like the Fun-ASR-Nano flagship model, require a GPU to run effectively. ## When NOT to use awesome-whisper - If looking for resources related to speech recognition systems from other providers not listed under OpenAI's Whisper ecosystem - In cases where the focus is on using pre-integrated solutions without the need for model customization ## Common questions ### What is the difference between FunASR and awesome-whisper? FunASR: Industrial-grade speech recognition toolkit. awesome-whisper: Curated resources for Whisper speech recognition system. See the comparison table for live GitHub stats and shared categories. ### When should I choose FunASR over awesome-whisper? Choose FunASR over awesome-whisper when License: FunASR is MIT, awesome-whisper is CC0-1.0; Requirements: Requires Python ≥ 3.8.; PyTorch and torchaudio must be installed separately before running FunASR.; Tags unique to FunASR: asr, audio, chinese, emotion-recognition; When requiring high-speed real-time processing of up to 170x realtime speech recognition. ### When should I choose awesome-whisper over FunASR? Choose awesome-whisper over FunASR when License: awesome-whisper is CC0-1.0, FunASR is MIT; Tags unique to awesome-whisper: ai, artificial-intelligence, gpt, openai; When seeking curated information on Whisper variants optimized for various platforms and languages. ### When should I avoid FunASR? In scenarios where only a narrow set of languages are required, as overhead for supporting over 50 languages may be unnecessary. For projects lacking GPU resources since certain models, like the Fun-ASR-Nano flagship model, require a GPU to run effectively. ### When should I avoid awesome-whisper? If looking for resources related to speech recognition systems from other providers not listed under OpenAI's Whisper ecosystem In cases where the focus is on using pre-integrated solutions without the need for model customization ### Is FunASR or awesome-whisper more popular on GitHub? FunASR has more GitHub stars (19,554 vs 2,361). Stars measure visibility, not whether either tool fits your constraints. ### Are FunASR and awesome-whisper open source? Yes - both are open-source projects on GitHub (FunASR: MIT, awesome-whisper: CC0-1.0). ### Where can I find alternatives to FunASR or awesome-whisper? GraphCanon lists graph-backed alternatives at [FunASR alternatives](/tools/modelscope-funasr/alternatives) and [awesome-whisper alternatives](/tools/sindresorhus-awesome-whisper/alternatives) ([FunASR markdown twin](/tools/modelscope-funasr/alternatives.md), [awesome-whisper markdown twin](/tools/sindresorhus-awesome-whisper/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/modelscope-funasr-vs-sindresorhus-awesome-whisper.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FunASR or awesome-whisper? FunASR: Very active. awesome-whisper: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for FunASR and awesome-whisper? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FunASR trust report](/tools/modelscope-funasr/trust); [awesome-whisper trust report](/tools/sindresorhus-awesome-whisper/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=modelscope-funasr`](/api/graphcanon/graph?tool=modelscope-funasr) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_